1 | #region License Information
|
---|
2 | /* HeuristicLab
|
---|
3 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
|
---|
4 | *
|
---|
5 | * This file is part of HeuristicLab.
|
---|
6 | *
|
---|
7 | * HeuristicLab is free software: you can redistribute it and/or modify
|
---|
8 | * it under the terms of the GNU General Public License as published by
|
---|
9 | * the Free Software Foundation, either version 3 of the License, or
|
---|
10 | * (at your option) any later version.
|
---|
11 | *
|
---|
12 | * HeuristicLab is distributed in the hope that it will be useful,
|
---|
13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
|
---|
14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
---|
15 | * GNU General Public License for more details.
|
---|
16 | *
|
---|
17 | * You should have received a copy of the GNU General Public License
|
---|
18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
|
---|
19 | */
|
---|
20 | #endregion
|
---|
21 |
|
---|
22 | using System;
|
---|
23 | using System.Linq;
|
---|
24 | using System.Threading;
|
---|
25 | using HeuristicLab.Common;
|
---|
26 | using HeuristicLab.Core;
|
---|
27 | using HeuristicLab.Data;
|
---|
28 | using HeuristicLab.Optimization;
|
---|
29 | using HeuristicLab.Parameters;
|
---|
30 | using HEAL.Attic;
|
---|
31 | using HeuristicLab.Problems.DataAnalysis;
|
---|
32 |
|
---|
33 | namespace HeuristicLab.Algorithms.DataAnalysis {
|
---|
34 | /// <summary>
|
---|
35 | /// Nearest neighbour classification data analysis algorithm.
|
---|
36 | /// </summary>
|
---|
37 | [Item("Nearest Neighbour Classification (kNN)", "Nearest neighbour classification data analysis algorithm (wrapper for ALGLIB).")]
|
---|
38 | [Creatable(CreatableAttribute.Categories.DataAnalysisClassification, Priority = 150)]
|
---|
39 | [StorableType("98161D6F-D977-45EA-B899-E47EE017865E")]
|
---|
40 | public sealed class NearestNeighbourClassification : FixedDataAnalysisAlgorithm<IClassificationProblem> {
|
---|
41 | private const string KParameterName = "K";
|
---|
42 | private const string NearestNeighbourClassificationModelResultName = "Nearest neighbour classification solution";
|
---|
43 | private const string WeightsParameterName = "Weights";
|
---|
44 | private const string SelfMatchParameterName = "SelfMatch";
|
---|
45 |
|
---|
46 | #region parameter properties
|
---|
47 | public IFixedValueParameter<IntValue> KParameter {
|
---|
48 | get { return (IFixedValueParameter<IntValue>)Parameters[KParameterName]; }
|
---|
49 | }
|
---|
50 | public IFixedValueParameter<BoolValue> SelfMatchParameter {
|
---|
51 | get { return (IFixedValueParameter<BoolValue>)Parameters[SelfMatchParameterName]; }
|
---|
52 | }
|
---|
53 | public IValueParameter<DoubleArray> WeightsParameter {
|
---|
54 | get { return (IValueParameter<DoubleArray>)Parameters[WeightsParameterName]; }
|
---|
55 | }
|
---|
56 | #endregion
|
---|
57 | #region properties
|
---|
58 | public bool SelfMatch {
|
---|
59 | get { return SelfMatchParameter.Value.Value; }
|
---|
60 | set { SelfMatchParameter.Value.Value = value; }
|
---|
61 | }
|
---|
62 | public int K {
|
---|
63 | get { return KParameter.Value.Value; }
|
---|
64 | set {
|
---|
65 | if (value <= 0) throw new ArgumentException("K must be larger than zero.", "K");
|
---|
66 | else KParameter.Value.Value = value;
|
---|
67 | }
|
---|
68 | }
|
---|
69 | public DoubleArray Weights {
|
---|
70 | get { return WeightsParameter.Value; }
|
---|
71 | set { WeightsParameter.Value = value; }
|
---|
72 | }
|
---|
73 | #endregion
|
---|
74 |
|
---|
75 | [StorableConstructor]
|
---|
76 | private NearestNeighbourClassification(StorableConstructorFlag _) : base(_) { }
|
---|
77 | private NearestNeighbourClassification(NearestNeighbourClassification original, Cloner cloner)
|
---|
78 | : base(original, cloner) {
|
---|
79 | }
|
---|
80 | public NearestNeighbourClassification()
|
---|
81 | : base() {
|
---|
82 | Parameters.Add(new FixedValueParameter<BoolValue>(SelfMatchParameterName, "Should we use equal points for classification?", new BoolValue(false)));
|
---|
83 | Parameters.Add(new FixedValueParameter<IntValue>(KParameterName, "The number of nearest neighbours to consider for regression.", new IntValue(3)));
|
---|
84 | Parameters.Add(new OptionalValueParameter<DoubleArray>(WeightsParameterName, "Optional: use weights to specify individual scaling values for all features. If not set the weights are calculated automatically (each feature is scaled to unit variance)"));
|
---|
85 | Problem = new ClassificationProblem();
|
---|
86 | }
|
---|
87 | [StorableHook(HookType.AfterDeserialization)]
|
---|
88 | private void AfterDeserialization() {
|
---|
89 | // BackwardsCompatibility3.3
|
---|
90 | #region Backwards compatible code, remove with 3.4
|
---|
91 | if (!Parameters.ContainsKey(WeightsParameterName)) {
|
---|
92 | Parameters.Add(new OptionalValueParameter<DoubleArray>(WeightsParameterName, "Optional: use weights to specify individual scaling values for all features. If not set the weights are calculated automatically (each feature is scaled to unit variance)"));
|
---|
93 | }
|
---|
94 | if (!Parameters.ContainsKey(SelfMatchParameterName)) {
|
---|
95 | Parameters.Add(new FixedValueParameter<BoolValue>(SelfMatchParameterName, "Should we use equal points for classification?", new BoolValue(false)));
|
---|
96 | }
|
---|
97 | #endregion
|
---|
98 | }
|
---|
99 |
|
---|
100 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
101 | return new NearestNeighbourClassification(this, cloner);
|
---|
102 | }
|
---|
103 |
|
---|
104 | #region nearest neighbour
|
---|
105 | protected override void Run(CancellationToken cancellationToken) {
|
---|
106 | double[] weights = null;
|
---|
107 | if (Weights != null) weights = Weights.CloneAsArray();
|
---|
108 | var solution = CreateNearestNeighbourClassificationSolution(Problem.ProblemData, K, SelfMatch, weights);
|
---|
109 | Results.Add(new Result(NearestNeighbourClassificationModelResultName, "The nearest neighbour classification solution.", solution));
|
---|
110 | }
|
---|
111 |
|
---|
112 | public static IClassificationSolution CreateNearestNeighbourClassificationSolution(IClassificationProblemData problemData, int k, bool selfMatch = false, double[] weights = null) {
|
---|
113 | var problemDataClone = (IClassificationProblemData)problemData.Clone();
|
---|
114 | return new NearestNeighbourClassificationSolution(Train(problemDataClone, k, selfMatch, weights), problemDataClone);
|
---|
115 | }
|
---|
116 |
|
---|
117 | public static INearestNeighbourModel Train(IClassificationProblemData problemData, int k, bool selfMatch = false, double[] weights = null) {
|
---|
118 | return new NearestNeighbourModel(problemData.Dataset,
|
---|
119 | problemData.TrainingIndices,
|
---|
120 | k,
|
---|
121 | selfMatch,
|
---|
122 | problemData.TargetVariable,
|
---|
123 | problemData.AllowedInputVariables,
|
---|
124 | weights,
|
---|
125 | problemData.ClassValues.ToArray());
|
---|
126 | }
|
---|
127 | #endregion
|
---|
128 | }
|
---|
129 | }
|
---|